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Article(id=1241759334259036168, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202404101, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1712419200000, receivedDateStr=2024-04-07, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773988095970, onlineDateStr=2026-03-20, pubDate=1727193600000, pubDateStr=2024-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773988095970, onlineIssueDateStr=2026-03-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773988095970, creator=13701087609, updateTime=1773988095970, updator=13701087609, issue=Issue{id=1241759317016252418, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='18', pageStart='3265', pageEnd='3456', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773988091898, creator=13701087609, updateTime=1773991617194, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241774103024174010, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241774103024174011, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241759317016252418, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3410, endPage=3415, ext={EN=ArticleExt(id=1241759336058392602, articleId=1241759334259036168, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Status and influencing factors of treatment delay of rifampicin resistant tuberculosis patients,Guizhou, 2016-2022, columnId=null, journalTitle=Modern Preventive Medicine, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective

This study assesses the status and determinants of treatment delays among patients with rifampicin-resistant tuberculosis (RR-TB) in Guizhou Province, 2016-2022, aiming to inform the development of RR-TB control strategies in the region.

Methods

Data on RR-TB patients from 2016 to 2022 were extracted from the "Tuberculosis Management Information System," a subsystem of the "China Disease Prevention and Control Information System." The trend in treatment delay rates over time was analyzed using Trend chi-square test, while logistic regression models were used to examine the factors influencing these delays.

Results

From 2016 to 2022, Guizhou Province reported 513 cases of treatment delays among patients with rifampicin-resistant tuberculosis (RR-TB), resulting in a treatment delay rate of 23.29%. The rate of treatment delays decreased from 2016 to 2019 (Trend chi-square test=9.099, P=0.003), reaching its lowest in 2018 at 10.53%. However, from 2020 to 2022, the rate of treatment delays exhibited an upward trend (Trend chi-square test=5.937, P=0.015), peaking in 2021 at 31.62%. Multivariable analysis indicated that compared to migrant populations, patients undergoing initial treatment, and those tested with molecular biological drug sensitivity tests, non-migrant patients (OR=1.496, 95%CI=1.198 to 1.867), new patients (OR=1.774, 95%CI =1.344 to 2.342), patients with relapse (OR=1.494, 95%CI=1.110 to 2.011), and those tested using traditional drug sensitivity methods (OR=2.985, 95%CI=2.323 to 3.834) were at higher risk for treatment delays.

Conclusion

From 2016 to 2022, the overall trend of treatment delays in rifampicin-resistant tuberculosis (RR-TB) patients in Guizhou Province showed a decline. However, there was an increasing trend during the COVID-19 pandemic. This suggests a need to enhance the prevention and control of drug-resistant tuberculosis in key areas such as Bijie City. There is also a need to intensify health education for patients with drug resistance, implement patient-centered care systems for those patients, expand the application of molecular biological drug sensitivity testing techniques, and strengthen the referral and admission mechanisms for RR-TB patients.

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目的

分析贵州省2016—2022年利福平耐药结核病(rifampicin-resistant tuberculosis，RR-TB)患者治疗延迟现状及其影响因素，为贵州省制定RR-TB防控措施提供依据。

方法

从“中国疾病预防控制信息系统”子系统“结核病管理信息系统”中导出贵州省2016—2022年RR-TB患者病案信息，治疗延迟率随时间变化趋势采用趋势χ2检验，采用logistic回归模型分析其影响因素。

结果

贵州省2016—2022年RR-TB发生治疗延迟患者513例，治疗延迟率为23.29%。2016－2019年患者治疗延迟率呈下降趋势(趋势χ2=9.099，P=0.003)，2018年治疗延迟率最低，为10.53%；2020－2022年患者治疗延迟率呈上升趋势(趋势χ2=5.937，P=0.015)，2021年最高为31.62%。在地区分布上，黔南州(50.00%)和毕节市(47.26%)的非流动患者的治疗延迟率位居前列；而遵义市(28.70%)的流动患者治疗延迟率最高。多因素分析结果显示，与流动人口、初治失败、使用分子生物学药敏检测的患者相比，非流动患者(OR=1.496,95%CI=1.198~1.867)、新患者(OR=1.774,95%CI=1.344~2.342)、复发(OR=1.494,95%CI=1.110~2.011)、使用传统药敏检测方法(OR=2.985,95%CI=2.323~3.834)是治疗延迟的危险因素。

结论

贵州省2016—2022年RR-TB患者治疗延迟率总体呈下降趋势，但受新型冠状病毒疫情影响仍然较高。建议进一步加强对毕节市等重点地区的防治工作，加强对耐药患者的健康教育，实施以患者为中心的耐药患者关怀服务体系；扩大医保报销范围等，降低患者经济负担；进一步扩大分子生物学检测技术应用，达到全覆盖水平，为患者诊治提供有利条件。

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李进岚，E-mail:
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=vXPmFkploH7Mc4s+3s9ZUg==, magXml=Sk3kUOvlu42NFKn/l7JhCA==, pdfUrl=null, pdf=5lxtHuWYnEg+ygqx+lZSNQ==, pdfFileSize=685561, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=iv07FxRpSopGz22xvnzWTQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=NzSQEyOWvKKcXVSRiABxxQ==, mapNumber=null, authorCompany=null, fund=null, authors=

王大福（1999—），男，硕士在读，研究方向：疾病预防与控制

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王大福（1999—），男，硕士在读，研究方向：疾病预防与控制

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Infectious Diseases of Poverty, 2020, 9(1): 49., articleTitle=Determinants of delayed diagnosis and treatment of tuberculosis in Cambodia: a mixed-methods study, refAbstract=null)], funds=[Fund(id=1241759372053910514, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, awardId=2020-181-131, language=CN, fundingSource=贵州省发改委省级基本建设前期工作项目(2020-181-131), fundOrder=null, country=null), Fund(id=1241759372309763072, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, awardId=null, language=CN, fundingSource=贵州省科技计划项目(黔科合基础-ZK[2022]一般389), fundOrder=null, country=null), Fund(id=1241759372502700045, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, awardId=81860591, language=CN, fundingSource=国家自然科学基金(81860591), fundOrder=null, country=null), Fund(id=1241759372615946261, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, awardId=2020-070, language=CN, fundingSource=贵州医科大学博士科研启动基金(2020-070), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241759344820293765, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, xref=1., ext=[AuthorCompanyExt(id=1241759344866431111, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, companyId=1241759344820293765, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, Guizhou 561113, China), AuthorCompanyExt(id=1241759345185198229, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, companyId=1241759344820293765, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113)]), AuthorCompany(id=1241759346690953395, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, xref=2., ext=[AuthorCompanyExt(id=1241759346699342004, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, companyId=1241759346690953395, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.贵州省疾病预防控制中心)])], figs=[ArticleFig(id=1241759368849462096, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=EN, label=Fig.1, caption=Treatment delays for migrant and non-migrant patients in various cities and autonomous regions, figureFileSmall=CtqL8ID2RBdO8piI1nVIwA==, figureFileBig=iv07FxRpSopGz22xvnzWTQ==, tableContent=null), ArticleFig(id=1241759368945931096, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=CN, label=图1, caption=各市州流动与非流动患者治疗延迟情况, figureFileSmall=CtqL8ID2RBdO8piI1nVIwA==, figureFileBig=iv07FxRpSopGz22xvnzWTQ==, tableContent=null), ArticleFig(id=1241759369411498872, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=EN, label=Table 1, caption=

Basic information of RR-TB patients

, figureFileSmall=null, figureFileBig=null, tableContent=
类别例数构成比(%)类别例数构成比(%)
性别男性1 50868.45重点人群是1536.95
女性69531.55否2 05093.05
民族汉族1 77480.53登记分类新患者68731.18
少数民族42919.47初治失败70231.87
流动人口否1 04547.44复发52423.74
是1 15852.56复治失败1376.22
年龄(岁)≤2441818.98其他1546.99
25 4480036.31治疗分类初治68731.18
45 6477435.13复治1 51768.82
≥652119.58合并其他结核是1104.99
职业学生873.95否2 09395.01
工人221.00检测方法传统35616.16
农民1 01846.21分子生物学1 84783.84
家务及待业77735.27
其他29913.57
), ArticleFig(id=1241759369625408391, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=CN, label=表1, caption=

RR-TB患者基本情况

, figureFileSmall=null, figureFileBig=null, tableContent=
类别例数构成比(%)类别例数构成比(%)
性别男性1 50868.45重点人群是1536.95
女性69531.55否2 05093.05
民族汉族1 77480.53登记分类新患者68731.18
少数民族42919.47初治失败70231.87
流动人口否1 04547.44复发52423.74
是1 15852.56复治失败1376.22
年龄(岁)≤2441818.98其他1546.99
25 4480036.31治疗分类初治68731.18
45 6477435.13复治1 51768.82
≥652119.58合并其他结核是1104.99
职业学生873.95否2 09395.01
工人221.00检测方法传统35616.16
农民1 01846.21分子生物学1 84783.84
家务及待业77735.27
其他29913.57
), ArticleFig(id=1241759369914815383, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=EN, label=Table 2, caption=

Delayed treatment in different subgroups of RR-TB patients

, figureFileSmall=null, figureFileBig=null, tableContent=
变量年份类别总例数治疗延迟例数治疗延迟率
(%)		χ2值P值
2016—2019年20161564730.139.0990.003
20171582817.72
20183043210.53
20192314419.05
2020—2022年20204098119.805.9370.015
202152516631.62
202242011527.38
PGDP(万元)高(>5.0)71214820.793.6820.159
中(3.5 5.0)87721524.52
低(<3.5)61415024.43
疫情严重程度(人/10万)高97824124.644.9540.084
中72117323.99
低5049919.43
), ArticleFig(id=1241759370095170464, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=CN, label=表2, caption=

不同亚组RR-TB患者治疗延迟情况

, figureFileSmall=null, figureFileBig=null, tableContent=
变量年份类别总例数治疗延迟例数治疗延迟率
(%)		χ2值P值
2016—2019年20161564730.139.0990.003
20171582817.72
20183043210.53
20192314419.05
2020—2022年20204098119.805.9370.015
202152516631.62
202242011527.38
PGDP(万元)高(>5.0)71214820.793.6820.159
中(3.5 5.0)87721524.52
低(<3.5)61415024.43
疫情严重程度(人/10万)高97824124.644.9540.084
中72117323.99
低5049919.43
), ArticleFig(id=1241759370288108458, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=EN, label=Table 3, caption=

Univariate analysis results of treatment delay in RR-TB patients

, figureFileSmall=null, figureFileBig=null, tableContent=
变量治疗延迟治疗延迟率
(%)		χ2值P值
延迟未延迟
性别
男性3451 15422.880.0950.758
女性15953622.88
年龄(岁)
≤248833021.052.7110.438
25~4419160923.88
45~6419058424.55
≥654416720.85
民族
汉族3901 38421.988.6480.003
少数民族12330628.67
流动人口
否30673929.2840.008<0.001
是20795117.88
职业
学生662124.1438.445<0.001
工人18418.18
农民73128728.19
家务及待业65312415.96
其他2227725.75
重点人群
是4710630.725.0850.024
否4661 58422.73
登记分类
新患者18250526.4930.609<0.001
初治失败11758516.67
复发13239225.24
复治失败3110622.63
其他5110333.12
治疗分类
初治18250526.495.7430.017
复治3311 18521.83
合并其他结核
有347630.913.7660.052
无4791 61422.89
传统16019644.94111.490<0.001
分子生物学3531 69019.11
地区
贵阳市6834616.4336.287<0.001
毕节市11633026.01
六盘水5813829.59
遵义市8821628.95
安顺市2313314.74
铜仁市5514727.23
黔西南州2510519.23
黔东南州3917018.66
黔南州4110528.08
), ArticleFig(id=1241759371173106626, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=CN, label=表3, caption=

RR-TB患者治疗延迟单因素分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量治疗延迟治疗延迟率
(%)		χ2值P值
延迟未延迟
性别
男性3451 15422.880.0950.758
女性15953622.88
年龄(岁)
≤248833021.052.7110.438
25~4419160923.88
45~6419058424.55
≥654416720.85
民族
汉族3901 38421.988.6480.003
少数民族12330628.67
流动人口
否30673929.2840.008<0.001
是20795117.88
职业
学生662124.1438.445<0.001
工人18418.18
农民73128728.19
家务及待业65312415.96
其他2227725.75
重点人群
是4710630.725.0850.024
否4661 58422.73
登记分类
新患者18250526.4930.609<0.001
初治失败11758516.67
复发13239225.24
复治失败3110622.63
其他5110333.12
治疗分类
初治18250526.495.7430.017
复治3311 18521.83
合并其他结核
有347630.913.7660.052
无4791 61422.89
传统16019644.94111.490<0.001
分子生物学3531 69019.11
地区
贵阳市6834616.4336.287<0.001
毕节市11633026.01
六盘水5813829.59
遵义市8821628.95
安顺市2313314.74
铜仁市5514727.23
黔西南州2510519.23
黔东南州3917018.66
黔南州4110528.08
), ArticleFig(id=1241759371416376272, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=EN, label=Table 4, caption=

Binary logistic regression analysis results

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变量类别BWald χ2POR(95%CI)
流动人口是1.000
否0.40312.624<0.0011.496(1.198~1.867)
职业学生1.000
工人-1.1310.0450.8320.878(0.262~2.935)
农民0.2000.5550.4561.221(0.722~2.068)
待业-0.2851.0570.3040.752(0.437~1.294)
其他0.1670.3200.5711.182(0.663~2.105)
登记分类初治失败1.000
新患者0.57316.351<0.0011.774(1.344~2.342)
复发0.4027.0160.0081.494(1.110~2.011)
复治失败0.2771.3700.2421.319(0.829~2.098)
其他0.5827.5510.0061.790(1.182~2.711)
检测方法分子生物学1.000
传统1.09373.222<0.0012.985(2.323~3.834)
), ArticleFig(id=1241759371680617436, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241759334259036168, language=CN, label=表4, caption=

二元logistic回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量类别BWald χ2POR(95%CI)
流动人口是1.000
否0.40312.624<0.0011.496(1.198~1.867)
职业学生1.000
工人-1.1310.0450.8320.878(0.262~2.935)
农民0.2000.5550.4561.221(0.722~2.068)
待业-0.2851.0570.3040.752(0.437~1.294)
其他0.1670.3200.5711.182(0.663~2.105)
登记分类初治失败1.000
新患者0.57316.351<0.0011.774(1.344~2.342)
复发0.4027.0160.0081.494(1.110~2.011)
复治失败0.2771.3700.2421.319(0.829~2.098)
其他0.5827.5510.0061.790(1.182~2.711)
检测方法分子生物学1.000
传统1.09373.222<0.0012.985(2.323~3.834)
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2016—2022年贵州省利福平耐药结核病患者治疗延迟现状及影响因素分析
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王大福 1 , 郭雪丽 1 , 马晓雪 2 , 王芸 1 , 周姣姣 1 , 周建 2 , 李福栋 1 , 李进岚 2, 1
现代预防医学 | 疾病预防控制 2024,51(18): 3410-3415
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现代预防医学 | 疾病预防控制 2024, 51(18): 3410-3415
2016—2022年贵州省利福平耐药结核病患者治疗延迟现状及影响因素分析
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王大福1, 郭雪丽1, 马晓雪2, 王芸1, 周姣姣1, 周建2, 李福栋1, 李进岚2, 1
作者信息
  • 1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113
  • 2.贵州省疾病预防控制中心

    • 王大福（1999—），男，硕士在读，研究方向：疾病预防与控制

通讯作者:

李进岚，E-mail:
Status and influencing factors of treatment delay of rifampicin resistant tuberculosis patients,Guizhou, 2016-2022
Da-fu WANG1, Xue-li GUO1, Xiao-xue MA2, Yun WANG1, Jiao-jiao ZHOU1, Jian ZHOU2, Fu-dong LI1, Jin-lan LI2, 1
Affiliations
  • School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, Guizhou 561113, China
出版时间: 2024-09-25 doi: 10.20043/j.cnki.MPM.202404101
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摘要
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目的

分析贵州省2016—2022年利福平耐药结核病(rifampicin-resistant tuberculosis，RR-TB)患者治疗延迟现状及其影响因素，为贵州省制定RR-TB防控措施提供依据。

方法

从“中国疾病预防控制信息系统”子系统“结核病管理信息系统”中导出贵州省2016—2022年RR-TB患者病案信息，治疗延迟率随时间变化趋势采用趋势χ2检验，采用logistic回归模型分析其影响因素。

结果

贵州省2016—2022年RR-TB发生治疗延迟患者513例，治疗延迟率为23.29%。2016－2019年患者治疗延迟率呈下降趋势(趋势χ2=9.099，P=0.003)，2018年治疗延迟率最低，为10.53%；2020－2022年患者治疗延迟率呈上升趋势(趋势χ2=5.937，P=0.015)，2021年最高为31.62%。在地区分布上，黔南州(50.00%)和毕节市(47.26%)的非流动患者的治疗延迟率位居前列；而遵义市(28.70%)的流动患者治疗延迟率最高。多因素分析结果显示，与流动人口、初治失败、使用分子生物学药敏检测的患者相比，非流动患者(OR=1.496,95%CI=1.198~1.867)、新患者(OR=1.774,95%CI=1.344~2.342)、复发(OR=1.494,95%CI=1.110~2.011)、使用传统药敏检测方法(OR=2.985,95%CI=2.323~3.834)是治疗延迟的危险因素。

结论

贵州省2016—2022年RR-TB患者治疗延迟率总体呈下降趋势，但受新型冠状病毒疫情影响仍然较高。建议进一步加强对毕节市等重点地区的防治工作，加强对耐药患者的健康教育，实施以患者为中心的耐药患者关怀服务体系；扩大医保报销范围等，降低患者经济负担；进一步扩大分子生物学检测技术应用，达到全覆盖水平，为患者诊治提供有利条件。

利福平耐药结核病  /  治疗延迟  /  影响因素  /  贵州省
Objective

This study assesses the status and determinants of treatment delays among patients with rifampicin-resistant tuberculosis (RR-TB) in Guizhou Province, 2016-2022, aiming to inform the development of RR-TB control strategies in the region.

Methods

Data on RR-TB patients from 2016 to 2022 were extracted from the "Tuberculosis Management Information System," a subsystem of the "China Disease Prevention and Control Information System." The trend in treatment delay rates over time was analyzed using Trend chi-square test, while logistic regression models were used to examine the factors influencing these delays.

Results

From 2016 to 2022, Guizhou Province reported 513 cases of treatment delays among patients with rifampicin-resistant tuberculosis (RR-TB), resulting in a treatment delay rate of 23.29%. The rate of treatment delays decreased from 2016 to 2019 (Trend chi-square test=9.099, P=0.003), reaching its lowest in 2018 at 10.53%. However, from 2020 to 2022, the rate of treatment delays exhibited an upward trend (Trend chi-square test=5.937, P=0.015), peaking in 2021 at 31.62%. Multivariable analysis indicated that compared to migrant populations, patients undergoing initial treatment, and those tested with molecular biological drug sensitivity tests, non-migrant patients (OR=1.496, 95%CI=1.198 to 1.867), new patients (OR=1.774, 95%CI =1.344 to 2.342), patients with relapse (OR=1.494, 95%CI=1.110 to 2.011), and those tested using traditional drug sensitivity methods (OR=2.985, 95%CI=2.323 to 3.834) were at higher risk for treatment delays.

Conclusion

From 2016 to 2022, the overall trend of treatment delays in rifampicin-resistant tuberculosis (RR-TB) patients in Guizhou Province showed a decline. However, there was an increasing trend during the COVID-19 pandemic. This suggests a need to enhance the prevention and control of drug-resistant tuberculosis in key areas such as Bijie City. There is also a need to intensify health education for patients with drug resistance, implement patient-centered care systems for those patients, expand the application of molecular biological drug sensitivity testing techniques, and strengthen the referral and admission mechanisms for RR-TB patients.

Rifampicin-resistant tuberculosis  /  Treatment delay  /  Influencing factors  /  Logistic regression  /  Guizhou Province
王大福, 郭雪丽, 马晓雪, 王芸, 周姣姣, 周建, 李福栋, 李进岚. 2016—2022年贵州省利福平耐药结核病患者治疗延迟现状及影响因素分析. 现代预防医学, 2024 , 51 (18) : 3410 -3415 . DOI: 10.20043/j.cnki.MPM.202404101
Da-fu WANG, Xue-li GUO, Xiao-xue MA, Yun WANG, Jiao-jiao ZHOU, Jian ZHOU, Fu-dong LI, Jin-lan LI. Status and influencing factors of treatment delay of rifampicin resistant tuberculosis patients,Guizhou, 2016-2022[J]. Modern Preventive Medicine, 2024 , 51 (18) : 3410 -3415 . DOI: 10.20043/j.cnki.MPM.202404101
正文
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利福平耐药结核病的广泛流行是21世纪人类所面临的一项重大挑战，该病具有治疗时间长、并发症和药物不良反应多、治疗费用多等问题[1]。据世界卫生组织指出，2022年全球新发RR-TB患者约41万例[2]。同时，我国是全球30个结核病高负担国家之一，2022年新发结核病患者74.8万人，占全球结核病患者的7.1%，位列全球第三位，RR-TB估算患者数约3万例[2]。结核病患者发生治疗延迟是阻碍结核病控制的重要因素，RR-TB患者发生治疗延迟可导致患者疾病恶化、死亡风险增加、耐药结核病进一步被传播等[3-6]。因此，本研究旨在了解贵州省2016—2022年RR-TB患者治疗延迟现况并分析其影响因素，为贵州省制定RR-TB防控措施提供依据。
1　资料与方法
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1.1　资料来源
资料来源于“中国疾病预防控制信息系统”子系统“结核病管理信息系统”中贵州省2016年1月1日至2022年12月31日登记确诊的RR-TB患者。
1.2　方法
从结核病管理信息系统中导出2016年1月1日至2022年12月31日RR-TB患者2 565例，排除未接受治疗和信息不完善患者362例，最终纳入本研究2 203例。收集信息包括患者基本情况（如性别、年龄、民族等）、治疗信息（如确诊时间、开始治疗时间等）。
1.3　相关定义
利福平耐药结核病：结核病患者感染的结核分枝杆菌经体外药敏试验（drug susceptibility testing，DST）证实对利福平耐药，无论是否对其他抗结核药物耐药[7-8]。治疗延迟：根据《中国结核病防治工作技术指南（2021版）》[7]中患者追踪程序（即确诊为RR-TB患者应在3 d内到辖区耐多药定点医院就诊；超过3 d未就诊者由区县级疾病预防控制中心5 d内进行电话追踪；追踪未到位者由基层医疗卫生机构7 d内进行现场追踪）同时结合既往研究[9-10]，将治疗延迟定义为开始治疗时间与确诊时间差≥14 d。治疗延迟率：治疗延迟患者占同期患者总人数的构成比。流动人口[11-12]指于各种原因离开户籍所在地到外省、市居住登记的肺结核病患者，在本研究中，将非本地户籍患者作为流动人口患者统计。登记分类包括[7]：（1）新患者：从未应用过抗结核药品治疗或应用抗结核药品治疗不足 1 个月（因其他疾病应用抗结核药品治疗除外）；（2）复发：指过去有明确的结核病史，完成规定的治疗疗程后医生认为已治愈，现在重新发病的患者；（3）初治失败：初治患者治疗第 5 个月末或疗程结束时，痰涂片或培养结果阳性；（4）复治失败：复治患者第 5 个月末或疗程结束时痰涂片或培养结果阳性；（5）其他：除上述以外的既往治疗或转归情况不明的肺结核患者。
以新型冠状病毒感染疫情大规模暴发划分为2016—2019年和 2020—2022年两个时间段。根据《贵州2022年统计年鉴》中各县区人均国内生产总值（real GDP per capita,PGDP）将贵州省88个县区划分为高PGDP（>5.0万元）、中PGDP（3.5~5.0万元）、低PGDP（<3.5万元）三类地区。根据结核病管理信息系统中贵州省2022年各县区结核病报告发病率，按疫情严重程度划分为高疫情地区（>100.00人/10万）、中疫情地区（67.00~99.99人/10万）、低疫情地区（<66.99人/10万）三类[13]。
1.4　统计学处理
使用SPSS 26.0软件进行统计分析。计数资料采用率或构成比表示，率或构成比的时间变化趋较采用趋势χ2检验；χ2检验用于率或构成比的组间差异比较；将单因素分析中P值小于0.2的变量纳入二元logistic回归模型进行影响因素分析，采用向前逐步条件法进行变量筛选，并计算各因素的比值比（odds ratio,OR）和95%置信区间（confidence interval, CI）,检验水准α=0.05。
2　结果
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2.1　基本情况
2 203例患者中，男性1 508例（68.45%）；25~65岁患者1 574例（71.545%）；汉族1 774例（80.53%）；流动患者1 158例（52.56%）；患者职业分布以农民1 018例（46.21%）、家务及待业777例（35.27%）位居前列，其它基本情况见表1。
2.2　治疗延迟时间变化趋势情况
2 203例RR-TB患者中，发生治疗延迟患者513例（23.29%）。2016—2019年患者治疗延迟率呈下降趋势（趋势χ2=9.099，P=0.003），2018年下降至最低，为10.53%；2020—2022年患者治疗延迟率呈上升趋势（趋势χ2=5.937，P=0.015），2021年上升至最高，为31.62%，见表2。
2.3　不同经济发展水平患者治疗延迟情况
低PGDP地区与中PGDP地区患者治疗延迟率相似，为分别为150例（24.43%）、215例（24.52%）；高PGDP地区患者最低，为148例（20.79%），但差异无统计学意义（χ2=3.682，P=0.159），见表2。
2.4　不同结核病疫情地区患者治疗延迟情况
研究发现，高疫情地区患者治疗延迟241例（24.64%）；中疫情地区治疗延迟173例（23.99%）；低疫情地区治疗延迟99例（19.43%），但差异无统计学意义（χ2=4.954，P=0.084），见表2。
2.5　患者治疗延迟情况的地区差异
根据患者流动情况进行分类表明，不同流动情况的患者治疗延迟率在各地区有显著差异，黔南州（50.00%）、毕节市（47.26%）非流动患者治疗延迟率位居前列；遵义市流动患者治疗延迟率最高，见图1。
2.6　单因素分析
单因素分析结果显示民族、流动人口、职业、重点人群、登记分类、治疗分类、药敏检测方法7个变量治疗延迟率差异均具有统计学意义（均有P<0.05），见表3。
2.7　多因素分析
多因素分析发现，与流动人口、初治失败、使用分子生物学药敏检测的患者相比，非流动患者（OR=1.496,95%CI=1.198~1.867）、新患者（OR=1.774,95%CI=1.344~2.342）、复发（OR=1.494,95%CI=1.110~2.011）、使用传统药敏检测方法（OR=2.985,95%CI=2.323~3.834）是患者发生治疗延迟的危险因素，见表4。
3　讨论
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本研究共纳入2 203例RR-TB患者，其中以男性、中青年、流动患者、农民、家务及待业为主。与其他研究结果类似[14-15]，主要与男性、中青年在家庭及社会中担任者重要角色，系家庭劳动力及经济来源，出入及环境复杂、不良生活习惯等有关而成为高危人群[16]。这提示需要更加关注这些高风险群体，加强患者健康教育工作，并针对此类患者的特定需求和挑战制定更有效干预措施和预防策略。
贵州省2016—2022年RR-TB患者治疗延迟率为23.29%，略高于王芸等[9]贵州省MDR-TB患者的22.7%，高于安徽省[17]肺结核患者的13.3%。可能与2020年新型冠状病毒感染疫情以来的交通管制、医疗资源不足导致结核病患者发现不及时、出行、就医等受阻有关；同时，相关研究显示[18-19]，经济水平是影响患者发生诊治延迟的重要因素，贵州属于西部省份，相对较低的经济发展水平和欠发达的医疗条件等可能是RR-TB患者治疗延迟率高于其他省份的原因。
从2016年至2022年，贵州省RR-TB患者的治疗延迟率逐渐下降。这一成就得益于“十三五结核病防治规划”的实施，贵州省不断增加对耐药结核病的关注和经费预算，推出了包括加大分子生物学快速药敏检测的覆盖范围、建立以患者为中心的综合防治体系、优化医疗保障体系在内的多项防控措施[20]。在地区分布上，黔南州和毕节市的非流动患者的治疗延迟率较高；而遵义市的流动患者治疗延迟率尤为显著。毕节市作为经济相对落后的西部地区，显示出治疗延迟与经济发展水平低下的关联[18]；相对经济较发达的遵义市，由于流动人口众多，许多耐药患者需要长途跋涉来此接受治疗，也因此导致较高的治疗延迟率。为解决此类问题以及提高患者纳入治疗率并改善患者的预后，贵州省在2022年实施了一系列新的医保政策以减轻患者的经济压力并消除其他治疗障碍。随后，于2023年推出“以耐药患者为中心”高效跨区域无缝对接收治管理新模式，系统化的规范了转诊、收治和病例追踪的各个环节，并为耐药肺结核患者建立了转诊绿色服务通道。
多因素logistic回归结果表明，非流动患者较流动患者更容易发生治疗延迟。对非流动患者进一步分析发现，农民、家务及待业患者在其中的占比达83.73%。相较于初治失败患者，新患者、复发、其他登记分类患者更容易发生治疗延迟，新患者可能因为症状较轻或未及时诊断为结核病而延迟就医；而复发患者由于之前的治疗经验可能产生心理障碍或对治疗失去信心，进而延后求治；此外，其他登记分类患者如慢性患者，可能因为病情复杂性或沟通不畅导致治疗推迟。使用传统药敏检测方法的患者较使用分子生物学快速药敏检测的患者更容易发生治疗延迟。相比于传统药敏试验，分子生物学快速检测技术能够大幅缩短利福平耐药检测时间，为RR-TB患者早期发现、治疗提供有利条件[21-23]。这提示各级疾病预防控制中心、定点医院、基础医疗机构应加强协作，加强对患者的健康教育工作，实施开展以患者为中心的全流程、全方位耐药患者关怀咨询服务；完善医保政策、扩大耐药患者医保报销范围，避免患者发生因病致贫、因病返贫；同时，应进一步扩大分子生物学快速药敏检测技术的覆盖范围。
本研究资料来源于结核病管理信息系统，虽数据可靠，但存在一定局限性，如排除未接受治疗和信息不完整患者（14%），可能对分析结果也会产生一定的影响；如患者家庭经济水平、结核病知晓情况等也可能是影响患者延迟治疗的因素，有待进一步开展调查。
综上所述，贵州省2016—2022年RR-TB患者治疗延迟率总体呈下降趋势，但受新型冠状病毒疫情影响任然较高；毕节市等的本地患者、遵义市的流动患者治疗延迟率较高；非流动患者、新患者、复发、其他登记分类、传统药敏检测方法是治疗延迟的重要危险因素。因此，建议进一步加强对毕节市等重点地区的防治工作，加强对耐药患者的健康教育，实施以患者为中心的耐药患者关怀服务体系；扩大医保报销范围等，降低患者经济负担；进一步扩大分子生物学检测技术应用，达到全覆盖水平，为患者诊治提供有利条件。
基金
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  • 贵州省发改委省级基本建设前期工作项目(2020-181-131)
  • 贵州省科技计划项目(黔科合基础-ZK[2022]一般389)
  • 国家自然科学基金(81860591)
  • 贵州医科大学博士科研启动基金(2020-070)
文献
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2024年第51卷第18期
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doi: 10.20043/j.cnki.MPM.202404101
  • 接收时间：2024-04-07
  • 首发时间：2026-03-20
  • 出版时间：2024-09-25
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  • 收稿日期：2024-04-07
基金
贵州省发改委省级基本建设前期工作项目(2020-181-131)
贵州省科技计划项目(黔科合基础-ZK[2022]一般389)
国家自然科学基金(81860591)
贵州医科大学博士科研启动基金(2020-070)
作者信息
    1.贵州医科大学公共卫生与健康学院，环境污染与疾病监控教育部重点实验室，贵州 贵阳 561113
    2.贵州省疾病预防控制中心

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李进岚，E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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